DESCRIPTION: The broad, long-term objective of this proposal is to develop a comprehensive understanding of transcription factor-chromatin interaction kinetics, and to integrate this kinetic information with transcription rates and regulatory processes, in vivo, genome-wide. An understanding of transcription on this level is highly relevant for human health. Precise transcriptional control is essential for normal cell growth and development, and numerous transcriptional defects have been linked to human diseases including cancer. In many cases, however, how a defect in a broadly active transcriptional regulator or co-regulator actually gives rise to disease is unclear especially on a dynamic level. Transcription is an inherently dynamic process. Recent studies have shown that RNA synthesis is stochastic, occurring in infrequent bursts. The degree of transcriptional stochasticity varies across genes suggesting that it is regulated. Remarkably, no method exists with sufficient temporal resolution (seconds to minutes) to measure the dynamics of transcription factors, which regulate RNA synthesis and its stochasticity at specific DNA sites. Aim 1 will expand an innovative experimental and physics-based computational method, crosslinking kinetics, which extracts DNA binding rates for transcription factors at specific loci to each binding site across a genome in vivo. Currently, no method exists for obtaining in vivo binding rates at specific loci on the second time scale. While a locus-specific method with low temporal resolution (many minutes to hours), competition ChIP, has been developed, the analysis methods only estimate relative turnover dynamics of the transcription factor. Aim 2 will develop a novel physics-based method to extract transcription factor-DNA binding rates from competition ChIP data at loci where the transcription factor turnover takes many minutes or more. We will compare the transcription factor-DNA binding rates derived by the two methods at hundreds of sites. This will allow us to further assess the influence of competitive and co-factor binding on the derived parameters and suggest generalized models which account for these effects. The proposed work will develop two methods for determining transcription factor-DNA binding rates on a genomic scale in vivo. This will break a significant methodological bottleneck and enable the study of the dynamics of transcriptional regulation in the cell.